This forms a critical component of the encoder. Methods for speech processing commonly operate on a variety of handcrafted input features. Such features include the Mel-frequency Cepstral Coefficients (MFCCs) or mel-spectrograms which are a frequency domain representations of speech signals [1]}. As an alternative to these hand-crafted features, neural networks-based representation learning models have been shown to produce more robust and generalizable features [2]}. Wav2vec [3]} is trained using a contrastive loss function in self-supervised way on large amounts of unlabeled audio data, resulting in latent representations that generalize into many tasks.
